ArticleScientific reports2025
A hybrid attention multi-scale fusion network for real-time semantic segmentation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A CNN-transformer dual-branch network with structure-aware loss for high-resolution edge detection.Scientific reports · 2026Article
- Real-time semantic segmentation of driving scenes via effective attention-based information fusion and hybrid encoder.Scientific reports · 2026Article
- Fusing Tool Segmentation Predictions from Pose-Informed Morphological Polar Transform of Endoscopic Images.IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In semantic segmentation research, spatial information and receptive fields are essential. However, currently, most algorithms focus on acquiring semantic information and lose a significant amount of spatial information, leading to a significant decrease in accuracy despite improving real-time inference speed. This paper proposes a new method to address this issue. Specifically, we have designed a new module (HFRM) that combines channel attention and spatial attention to retrieve the spatial information lost during downsampling and enhance object classification accuracy. Regarding fusing spatial and semantic information, we have designed a new module (HFFM) to merge features of two different levels more effectively and capture a larger receptive field through an attention mechanism. Additionally, edge detection methods have been incorporated to enhance the extraction of boundary information. Experimental results demonstrate that for an input size of 512 × 1024, our proposed method achieves 73.6% mIoU at 176 frames per second (FPS) on the Cityscapes dataset and 70.0% mIoU at 146 FPS on Camvid. Compared to existing networks, our Model achieves faster inference speed while maintaining accuracy, enhancing its practicality.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.